Python+Docker实现人脸识别远程监控系统

Python , , , 评论

说明

本项目实现远程人脸识别监控功能，包括服务端程序和客户端程序。服务端使用Python OpenCV库实现人脸识别功能，将添加上识别结果的视频流通过TCP发送给客户端，且实现了Docker容器对上述功能的封装；客户端程序通过IP+端口接入服务端，接收视频流，实时查看人脸识别结果。客户端可与服务端断开接入后重新连接，不影响服务端功能。

项目文件

  • tcpsend.py（服务端程序）
  • tcpreceive.py（客户端程序）
  • requirements.txt（项目依赖）
  • train.py（人脸识别模型训练程序）
  • trainer.yml（人脸识别模型）
  • haarcascade_frontalface_alt2.xml（分类器）
  • Dockerfile（构建Docker镜像所需文件）

项目源码

tcpsend.py

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import socket
import cv2
import sys
import os
import numpy as npip
###
address = ('ip', 5555)  # 服务端地址和端口
ser = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
ser.bind(address)
ser.listen(5)
# 阻塞式
print('waiting。。。')
conn, addr = ser.accept()
print('建立连接...')
print('连接对象：', addr)
cap = cv2.VideoCapture(0)
# cap = cv2.VideoCapture(0)
frames_num=cap.get(7)
print('视频总帧数：',frames_num)
print('发送目标...')
count = 0
###
###
#加载训练数据集文件
recogizer = cv2.face.LBPHFaceRecognizer_create()
recogizer.read('./trainer.yml')
names = []
warningtime = 0
#准备识别的图片
def face_detect_demo(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # 转换为灰度
    face_detector = cv2.CascadeClassifier('./haarcascade_frontalface_alt2.xml')
    face = face_detector.detectMultiScale(gray, 1.1, 5, cv2.CASCADE_SCALE_IMAGE, (100, 100), (300, 300))
    # face=face_detector.detectMultiScale(gray)
    for x, y, w, h in face:
        cv2.rectangle(img, (x, y), (x+w, y+h), color=(255, 144, 30), thickness=2)  # blue,green,red
        # cv2.circle(img,center=(x+w//2,y+h//2),radius=w//2,color=(0,255,0),thickness=1)
        # 人脸识别
        ids, confidence = recogizer.predict(gray[y:y + h, x:x + w])
        # print('标签id:',ids,'置信评分：', confidence)
        if confidence > 80:
            cv2.putText(img, 'unkonw', (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 144, 30), 2)
        else:
            cv2.putText(img, 'dong', (x + 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (255, 144, 30), 2)
    # cv2.imshow('result', img)
    # print('bug:',ids)
    return img
def name():
    path = './dataset/'
    #  names = []
    imagePaths = [os.path.join(path, f) for f in os.listdir(path)]
    for imagePath in imagePaths:
       name = str(os.path.split(imagePath)[1].split('.', 2)[1])
       names.append(name)
###
#name()
while True:
    flag, frame = cap.read()
    frame = face_detect_demo(frame)
    # frame = cv2.resize(frame, (1280, 720))
    # cv2.imshow('send', frame)
    cv2.waitKey(1)
    count += 1
    # 数据打包有很多方式，也可以用json打包
    img_encode = cv2.imencode('.jpg', frame)[1]
    data_encode = np.array(img_encode)
    str_encode = data_encode.tostring()
    try:
        conn.sendall(str_encode)
    except :
        conn, addr = ser.accept()

tcpreceive.py

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import socket
import sys
import cv2
import numpy as np
import time
address = ('ip', 5555)  # 服务端地址和端口
cli = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
try:
    cli.connect(address)  # 尝试连接服务端
except Exception:ip
    print('[!] Server not found or not open')
    sys.exit()
frame_count = 1
while True:
    time1 = time.time() if frame_count == 1 else time1
    trigger = 'ok'
    cli.sendall(trigger.encode())
    data = cli.recv(1024*1024*20)
    image = np.frombuffer(data, np.uint8)
    image = cv2.imdecode(image, cv2.IMREAD_COLOR)
    try:
        cv2.imshow('video', image)
    except:
        continue
    cv2.waitKey(1)
    end_time = time.time()
    time2 = time.time()
    print(image.shape[:2], int(frame_count / (time2 - time1)))
    frame_count += 1
cli.close()

train.py

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import os
import cv2 as cv
from PIL import Image
import numpy as np

def getImageAndLabels(path):
    facesSamples = []  # 储存人脸数据
    ids = []  # 尺寸姓名数据
    imagePaths = [os.path.join(path, f) for f in os.listdir(path)]  # 储存图片信息
    face_detector = cv.CascadeClassifier('D:/Python/python3.9.8/Lib/site-packages/cv2/data/haarcascade_frontalface_alt2.xml')  # 加载分类器
    for imagePath in imagePaths:  # 遍历列表中的图片
        PIL_img = Image.open(imagePath).convert('L')  # 打开图片，灰度化(PIL有九种模式：1,L,P,RGB,RGBA,CMYK,YCbCr,YCbCr,I,F)
        img_numpy = np.array(PIL_img, 'uint8')  # 将图片转换为数组，以黑白深浅
        faces = face_detector.detectMultiScale(img_numpy)  # 获取图片人脸特征
        id = int(os.path.split(imagePath)[1].split('.')[0])  # 获取每张图片的id和姓名
        for x, y, w, h in faces:
            ids.append(id)
            facesSamples.append(img_numpy[y:y+h, x:x+w])
    print('id:', id)
    print('fs:', facesSamples)
    return facesSamples, ids

if __name__ == '__main__':
    path = './dataset/'  # 图片路径
    faces, ids = getImageAndLabels(path)  # 获取图像数组和id标签数据和姓名
    recognizer = cv.face.LBPHFaceRecognizer_create()  # 加载识别器
    recognizer.train(faces, np.array(ids))  # 训练
    recognizer.write('./trainer/trainer.yml')  # 保存文件

requirements.txt

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numpy==1.21.5
Pillow==9.1.0
opencv-contrib-python==4.5.5.64
opencv-python==4.5.5.64

Dockerfile

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FROM python:3.7-slim
ADD ./facerecognitionserver /code
WORKDIR /code
RUN apt-get update
RUN apt-get install -y libgl1-mesa-glx
RUN apt-get install -y libglib2.0-dev
RUN pip install -r requirements.txt
CMD ["python","/code/tcpsend.py"]

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